Data & AI Workflows

What this service covers

I help design and deliver AI-enabled systems that do something concrete: internal knowledge assistants, document and policy search, workflow copilots, structured extraction pipelines, support tooling, or agentic processes that combine LLMs with business rules, APIs, and human review.

The work can include use-case definition, prompt and workflow design, retrieval architecture, tool integration, model selection, evaluation, guardrails, governance, and deployment patterns for both cloud and self-hosted environments.

When a team wants to move quickly from idea to working agent, I also work with LangChain as the application layer and LangGraph when the workflow needs durable execution, human-in-the-loop control, and richer orchestration.

LangGraph becomes especially relevant when the process needs resumable state, explicit pauses for approval, or deterministic replay around side effects and long-running steps.

LangSmith is the observability layer I recommend when teams need tracing, debugging, evaluation, and deployment visibility for those AI workflows.

For retrieval-heavy systems built on PostgreSQL, I also work with pgvector when teams want embeddings, semantic search, and RAG support close to the rest of their data.

When the retrieval layer needs a dedicated vector database, I can also work with Qdrant as the alternative for teams that want a separate retrieval platform instead of keeping vectors inside Postgres.

With Qdrant, that can include collection design, payload-based multitenancy, hybrid dense-and-sparse retrieval, and collection aliases for safer index or version swaps.

When the use case calls for a chat-first agent platform, I also work with systems such as OpenClaw that combine persistent memory, channels, skills, and machine-level execution into a single operational loop.

Typical outcomes

• clearer identification of where AI creates leverage and where it adds unnecessary risk
• internal copilots or assistants connected to real documents, systems, and workflows
• agentic automation that combines LLM reasoning with APIs, approvals, and operational constraints
• retrieval and knowledge workflows that reduce search friction and repeated manual work
• more realistic AI adoption grounded in evaluation, observability, and maintainability

Typical fit

This service is a strong fit for organizations that want to move beyond generic AI experimentation and build something useful, accountable, and integrated into actual delivery, support, operations, or internal knowledge work.